Gini Loh System Tools

AI Automation Index

Will AI Replace Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders?

Task-level automation exposure for Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders: 11 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

21% weighted task index

11 of 11 tasks scored

Median Salary

$43,530 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

14,760 people hold this job — size, not growth

Projected 2025–35

+3.4% employment change

1,700 openings a year · BLS line item

Pay by state

BLS OEWS publishes 40 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$22,620 – $64,610

Highest medians

Maine $64,610
Wisconsin $54,290
Oregon $49,130
North Dakota $48,240
Pennsylvania $48,000

Lowest medians

Puerto Rico $22,620
Idaho $33,660
Mississippi $34,280
Alabama $35,090
New Mexico $35,600

State figures come from the 51-9192 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 21%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 71%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 0%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 9%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 0%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 10/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

11 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 9%

1 tasks

Medium exposure (≥ 35%) 0%

0 tasks

Low exposure (below 35%) 91%

10 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Constituent Tasks

#14368 · Core · 10% automatable

Add specified amounts of chemicals to equipment at required times to maintain solution levels and concentrations.

physical manipulation — Applying solutions to equipment is manual

Apply solutions to production equipment.
#14372 · Core · 30% automatable

Observe machine operations, gauges, or thermometers, and adjust controls to maintain specified conditions.

visual inspection — Instrument monitoring needs physical sensor presence

Monitor instruments to ensure proper production conditions.
#14373 · Core · 17% automatable

Set controls to regulate temperature and length of cycles, and start conveyors, pumps, agitators, and machines.

physical manipulation — Manual temperature control adjustment

Adjust temperature controls of ovens or other heating equipment. Operate pumping systems or equipment.
#14369 · Core · 5% automatable

Drain, clean, and refill machines or tanks at designated intervals, using cleaning solutions or water.

physical manipulation — Hands-on cleaning of equipment in unstructured settings

Clean production equipment.
#14367 · Core · 15% automatable

Operate or tend machines to wash and remove impurities from items such as barrels or kegs, glass products, tin plate surfaces, dried fruit, pulp, animal stock, coal, manufactured articles, plastic, or rubber.

physical manipulation — Hands-on control of industrial machinery in unstructured settings

Operate industrial equipment.
#14375 · Core · 82% automatable

Record gauge readings, materials used, processing times, or test results in production logs.

data analysis — Record production data into structured logs

Record operational or production data.
#14374 · Core · 18% automatable

Examine and inspect machines to detect malfunctions.

visual inspection — Physical inspection of production machinery in situ

Inspect production equipment.
#14370 · Core · 19% automatable

Measure, weigh, or mix cleaning solutions, using measuring tanks, calibrated rods or suction tubes.

physical manipulation — Physical measuring and dispensing of ingredients

Measure ingredients or substances to be used in production processes. Mix substances to create chemical solutions.
#14371 · Supplemental · 15% automatable

Draw samples for laboratory analysis, or test solutions for conformance to specifications, such as acidity or specific gravity.

physical manipulation — Chemical/physical testing requires lab manipulation

Test chemical or physical characteristics of materials or products. Collect samples of materials or products for testing.
#14376 · Supplemental · 8% automatable

Adjust, clean, and lubricate mechanical parts of machines, using hand tools and grease guns.

physical manipulation — Manual lubrication of production equipment

Clean production equipment. Lubricate production equipment.
#14377 · Supplemental · 5% automatable

Load machines with objects to be processed and unload them after cleaning, placing them on conveyors or racks.

physical manipulation — Physically extracting parts from production equipment

Load materials into production equipment. Remove products or workpieces from production equipment.
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Community Sentiment

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Method

How this was scored

  1. 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
  2. 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
  3. 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
  4. 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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